Now that you have learned how to compile statistics of letter correlation and word
correlation in model natural language text, put it to work having the computer generate
text of its own. Learn how to write programs that create sequences of letters or words
based on randomly generated transitions to complete N-grams.

Hybrid search is a new way to use IBM Watson Assistant without some of the limitations of a traditional chatbot. This approach lets you have best of both worlds: a robust search engine, which is coupled with AI technology that is capable of evolving as Watson receives additional training over time.

Build on the concept of N-grams of sequential letters to look at N-grams of words, and the statistics that can be derived from these. Learn how to generate graphical plots of N-gram frequencies. Explore the American National Corpus as an enormous and rich source of English text suitable for general-purpose language modeling tasks.

Watson Natural Language Classifier is part of the IBM Watson cognitive services
platform on IBM Cloud. In this article, you'll use Watson Natural Language Classifier on
IBM Cloud to create, train, and test the accuracy of a spam classification
service.

This tutorial demonstrates how to implement a custom crawler plug-in with IBM Watson
Explorer to analyze a hierarchical data structure within the context of content
analytics. The strategy outlined here permits the retaining of the hierarchical
structure or grains of the model being analyzed.

AI is more than pattern recognition. It can also build on patterns to generate expression. This is increasingly important in the world of intelligent agents. Learn about generative AI, an important class of techniques to the modern developer. As a first step, consider the patterns in natural language and how these can be modeled to prepare machines to generate their own expressions of familiar language. Discover how to go from basic letter frequency statistics to correlation between letters by using matrix-based models.

In this tutorial series, you'll learn how to use Docker, Eclipse, and IBM Cloud to
develop, extend, and host your own Minecraft servers. Find out how to use Eclipse to
build Minecraft plugins, test them locally using Docker, and use IBM Cloud to host your
Docker containers on the Internet. You'll also harness the power of IBM Watson from
within Minecraft for more educational and interactive game play. In Part 4, you'll learn
how to extend the Spigot server with a plugin that uses Watson cognitive services to add
a little science to your game play.

If you're looking to get started with chatbots, Watson services in
IBM Cloud make it easy. In this tutorial, you'll learn both basic and advanced
techniques for building chatbots that respond intelligently to your users.

This article is the fifth in a five-part series, "Developing cognitive
IoT solutions for anomaly detection by using deep learning." This article
demonstrates a deep learning solution using Keras and TensorFlow and how it is
used to analyze the large amount of data that IoT sensors gather.

Cognitive computing is becoming increasingly important within the enterprise. In
this fourth tutorial in a series, we discuss the primary methodologies and patterns used
to build cognitive solutions for the telecommunications and media and entertainment
industries.

Discover better insights with higher confidence faster than humanly
possible. It is the key to taking a first step in the right cognitive
application direction: asking not what could it do but what it should do. With
artificial intelligence (AI), we have decades of data and research into human
thought processes and communication to use a blueprint. To simulate human
relationships, we begin by observing and better understanding
ourselves.

Originally developed as a Python wrapper for the LuaJIT-based Torch
framework, PyTorch, now a native Python package, redesigns and implements
Torch in Python while sharing the same core C libraries for the back-end code.
Get to know PyTorch.

In this tutorial, discover some of the more useful applications for
visualizing data and a few of the approaches you can use to create that
visualization, including the R programming language, gnuplot, and Graphviz.

In this lessons learned article, discover how this lifetime hacker
hacked together emerging IoT and cognitive technologies to create a
proof-of-concept for an age-in-place healthcare scenario. The lessons
included: cognitive IoT apps must be learning systems; context defines
success; and you don't always know what you don't know.

This article gives you a quick overview of Keras, a Python-based,
deep-learning library. Learn about the framework's benefits, supported
platforms, installation considerations, and supported back ends.

Eclipse Deeplearning4j (DL4j) is a framework of deep learning tools and
libraries that take advantage of the Java Virtual Machine, making it easier to
deploy deep learning in enterprise big data applications.

The first part of this series introduced how to migrate from Watson
Retrieve and Rank to Watson Discovery service using original source data. In
this part, we'll look at how to migrate applications by taking data directly
from Watson Retrieve and Rank and adding it to Watson Discovery.

Explore an outline of a "cognitive DevOps" process that refines and adapts the
best parts of DevOps for new cognitive or artificial intelligence applications.
Specifically, the tutorial covers applying DevOps to the training process of cognitive
systems including training data, modeling, and performance evaluation.

This article is the fourth in a five-part series, "Developing cognitive
IoT solutions for anomaly detection by using deep learning." This article
demonstrates a deep learning solution using Apache SystemML and how it is used
to analyze the large amount of data that IoT sensors gather.

This tutorial builds upon the "Create a fun, simple IoT accelerometer
game" tutorial. It shows how to capture 3 different types of movement data
(instead of just 1), it shows how to send that data to the IBM Cloud using IBM
Watson IoT Platform, and finally it shows how to analyze that data with the
Watson Machine Learning service and SPSS Modeler.

TensorFlow is just one of the many open source software libraries for
machine learning. In this tutorial, get an overview of TensorFlow, learn which
platforms support it, and look at installation considerations.

Learn about reinforcement learning, a subfield of machine learning with
which you can train software agents to behave rationally in an
environment. In this article, you'll delve into the technology and discover
some of the problem areas to which you can apply it.

This tutorial guides you through the process of creating and training a
Watson Discovery Service with sample data. This tutorial uses the same data
set used in the Retrieve and Rank "Getting Started Tutorial" but you can use the same
approach to create a service instance that uses your own data.

Apply the iterative software development lifecycle (SDLC) to data for artificial intelligence (AI) and cognitive applications. Improve your systems for sourcing and assessment of data sets, and controlling dimensionality, all the way through the evaluation that feeds each iteration in the cycle.

Gain a sound understanding of the crucial role of data in the development of artificial intelligence and cognitive applications, and how this importance has developed throughout the history of AI, though not always explicitly acknowledged. Learn how the quality and quantity of available data can make all the difference in pattern analysis and training. AI is experiencing a resurgence on the web, but the understanding that a good data corpus is the lifeblood of any AI is not widespread. Learn to avoid the enormous danger from AI doing more harm than good if problems of bias and statistical skew propagate from the data corpus. Gain an edge in developing successful AI applications by understanding the role of data in various AI techniques, and the characteristics of data sets that support those techniques.

In this tutorial series, you'll learn how to use Docker, Eclipse, and
IBM Cloud to develop, extend, and host your own Minecraft servers. Find out how
to use Eclipse to build Minecraft plugins, test them locally using Docker, and
use IBM Cloud to host your Docker containers on the Internet. You'll also
harness the power of IBM Watson from within Minecraft for more educational and
interactive game play. In Part 3, you learn how to take the plugin that you
built in Part 2 to the next level -- by getting it running on the web in
IBM Cloud.

From a self-learning checkers game to IBM Watson playing Jeopardy!, artificial
intelligence (AI) has been an intense focus of computer research. Learn more about the
history of AI and the languages that have advanced its use and capabilities.

Obtain, run, and extend a Node.js starter application that uses the
IBM Cloud Geospatial Analytics service. With the Geospatial Analytics service,
you can monitor moving devices from the Internet of Things. The service
analyzes a device message stream from MQTT and tracks device locations in real
time with respect to one or more geographic regions.

Discover the range and types of deep learning neural architectures and networks,
including RNNs, LSTM/GRU networks, CNNs, DBNs, and DSN, and the frameworks to help get
your neural network working quickly and well.

This code shows you how to use the Java API for the Watson language
translator service. Given some text, a source language, and a target language,
Watson translates that text and returns one or more translations to
you.

This code shows you how to use the node.js API for the Watson
Personality Insights service. Given some text, Watson analyzes the openness,
conscientiousness, extraversion, agreeableness, emotional range, and needs of
the speaker.

This code shows you how to use the node.js API for the Watson Tone
Analyzer service. Given some text, Watson evaluates the tone, looking for
qualities such as the speaker's levels of anger, disgust, joy, fear, and
sadness.

Train your private search collection by using Relevancy Training so that users
can get the right answer to their question faster. See how Watson uses machine learning techniques to find
specific signals in queries that can be applied against the corpus.

Get an introduction to IBM Watson
for Real World Evidence, a cloud-based interactive Watson Health Life Sciences
platform for decision makers, analysts, and data scientists to generate and test
hypotheses.

Get an introduction to IBM Watson
for Real World Evidence, a cloud-based interactive Watson Health Life Sciences
platform for decision makers, analysts, and data scientists to generate and test
hypotheses.

Get an introduction to IBM Watson
for Real World Evidence, a cloud-based interactive Watson Health Life Sciences
platform for decision makers, analysts, and data scientists to generate and test
hypotheses.

Add language translation to your IBM Cloud apps. Use Node-RED and the
Language Translation service to create an app that translates text that the
user enters and performs sentiment analysis on that text.

This article is the second in a five-part series, "Developing cognitive
IoT solutions for anomaly detection by using deep learning." This article is a
tutorial about using Node-RED to create a test data simulator.

In this tutorial series, you'll learn how to use Docker, Eclipse, and
IBM Cloud to develop, extend, and host your own
Minehttp://www.ibm.com/developerworks/i/twitterdw-26796-minecraftseries.jpgcraft
servers. Find out how to use Eclipse to build Minecraft plugins, test them
locally using Docker, and use IBM Cloud to host your Docker containers on the
Internet. You'll also harness the power of IBM Watson from within Minecraft
for more educational and interactive game play. In Part 1, you'll set up your
local Minecraft and Docker development environment, and see the power of
Docker for building custom servers for Minecraft. You'll even get started
playing with Minecraft on your own locally hosted server!

In this tutorial series, you'll learn how to use Docker, Eclipse, and
IBM Cloud to develop, extend, and host your own Minecraft servers. Find out how
to use Eclipse to build Minecraft plugins, test them locally using Docker, and
use IBM Cloud to host your Docker containers on the Internet. You'll also
harness the power of IBM Watson from within Minecraft for more educational and
interactive game play. In Part 2, you'll set up your local development
environment in Eclipse, then develop, build, and export your own server-side
Minecraft plugin into a local Docker image.

This article is the third in a five-part series, "Developing cognitive
IoT solutions for anomaly detection by using deep learning." This article
demonstrates a deep learning solution using Deeplearning4j and how it is used
to analyze the large amount of data that IoT sensors gather.

This code shows you how to use the node.js API for the Watson Language
Translator service. Given some text, a source language, and a target language,
Watson translates that text and returns one or more translations to
you.

IBM introduced Watson services to the IBM Bluemix platform in early
October 2014. This tutorial introduces the services and SDK currently
available and describes how to deploy an application using the Watson Question
and Answer service on Bluemix. The deployed application is a Java-based
application.

This code shows you how to use the Java API for the Watson natural
language classification service. Given some text and a context, Watson
analyzes the text and returns a list of categories relevant to that
text.

This code shows you how to use the node.js API for the Watson natural
language classification service. Given some text and a context, Watson
analyzes the text and returns a list of categories relevant to that
text.

This code shows you how to use the Java API for the
Watson Personality Insights service. Given some text, Watson analyzes
the openness, conscientiousness, extraversion, agreeableness,
emotional range, and needs of the speaker.

This code shows you how to use the Java API for the Watson tone analysis
service. Given some text, Watson evaluates the tone, looking for qualities
such as the speaker's levels of anger, disgust, joy, fear, and
sadness.

You can add powerful abilities to your IBM i applications by using IBM Bluemix Watson
Services. The article illustrates how to create a Watson Language Translator service and
obtain the credentials for accessing that service. It then provides several SQL statements
that are used to access the translator service from IBM i. The article also describes how a
Java program can be used to access the Watson Language Translator service.

The tutorial shows how a mobile application can use the Watson Assistant, Text
to Speech, and Speech to Text services to understand user commands, which are then used
to control devices through IBM IoT Platform services. It also shows how to integrate a
Raspberry Pi as a home gateway that receives commands from and sends events to the
mobile app. Finally, it shows how to store images by using Object Storage
Service.

Get an overview of the history of artificial intelligence as well as the latest in neural network
and deep learning approaches. Learn why, although AI and machine learning have had their
ups and downs, new approaches like deep learning and cognitive computing
have significantly raised the bar in these disciplines.

With the growth in applications that exploit the power of deep learning,
artificial intelligence technologies are adding value to markets and
applications across the board. This article explores five key concepts you
should consider when developing an intelligent application.

This article is the first in a five-part series, "Developing cognitive
IoT solutions for anomaly detection by using deep learning." This article
explains what deep learning is, what neural networks are, and how they can be
used to analyze the large amount of data that IoT sensors gather.

This tutorial explains how our team used IBM Bluemix, the Watson Natural Language Understanding API, crawled web data, and Twitter data to create a web page to track the aggregated sentiment signal for several publicly traded firms. We also give two possible uses of this data in equity research.

The Watson Visual Recognition service enables you to leverage cognitive computer vision to extract information from any image library. You can even go beyond standard object classification and use what are called ‘custom classifiers’ to train the Watson service to recognize specific items or conditions of your choosing. Read how the Watson Visual Recognition command line interface utility is used
to interact with the service to train and test your custom classifiers.

The Watson Conversation Service offers a simple, scalable and science-driven solution for developers to build powerful chat bots to address the needs of various brands and companies.
As developers leverage Watson Conversation to build cognitive solutions for various, one recurring question is: “How much time should I plan to train my solution” or “How do I know when my model is trained sufficiently well”? While the answer depends greatly on the problem being solved and the data powering the solution, in this blog we offer a common methodology for training the machine learning (ML) models powering your chat bot solution.

Despite the exponential growth in chatbots across various applications and messaging platforms, there are still several challenges to overcome when delivering a successful chatbot. One of the key challenges is the ability of the chatbot to understand the wide variety of inputs from the users. In this blog, we focus on computing and evaluating performance metrics for the trained machine learning system powering the chatbot.

You’ve probably heard of Blockchain. It’s hard to navigate much of the web today without running across some kind of reference to it. After a while I thought, “Could I really explain Blockchain to someone if asked?” and if you’re in the same boat as I was, this post is for you.

IBM has extracted the core machine learning technology from IBM Watson and will initially make it available where much of the world’s enterprise data resides: the z System mainframe, the operational core of global organizations where billions of daily transactions are processed.

Create your own fully functional chatbot to deliver news and articles. This series shows you how to do this using two different messaging applications: Facebook Messenger and Slack. It then explains how to use IBM Watson services to enhance your chatbot. This tutorial explains how to build the chatbot to work with Facebook Messenger.

Create your own fully functional chatbot to deliver news and articles. This series shows you how to do this using two different messaging applications: Facebook Messenger and Slack. It then explains how to use IBM Watson services to enhance your chatbot. This tutorial explains how to build the chatbot to work with Slack.

Create your own fully functional chatbot to deliver news and articles. This series
shows you how to do this using two different messaging applications: Facebook Messenger
and Slack. It then explains how to enhance your chatbot by using IBM Watson services.
This third tutorial in the series explains the IBM Watson services that are used to
enhance the chatbot.

In this series, learn how to create a translation application with a speech to
text front end. The tutorials in the series cover how to set up your environment and
then use Java programming to develop the cognitive services.

Cognitive computing is becoming increasingly important within the enterprise. In this
tutorial, the third in a series, look at the design patterns for making cognitive data searchable and understandable.

In this series, learn how to create a translation application with a speech to
text front end. The tutorials in the series cover setting up your environment and then
using Java programming to develop the cognitive services.

Cognitive computing is becoming increasingly important within the enterprise. In this
tutorial, the second in a series, explore multiple
successful cognitive use cases to help you understand what can be accomplished by
building a cognitive platform.

Cognitive computing is becoming increasingly important within the enterprise. In this
tutorial, the first in a series, learn how you can design and implement cognitive solutions in
your own environment.

Quickly build an app to get data from Twitter and then use the data for cognitive insights by using IBM Watson services such as Tone Analyzer, Visual Recognition, and Alchemy. In just a few minutes, you can create a running Python app on Bluemix that analyzes pictures that are included in tweets.

IBM Watson Message Insights and Sentiment Analysis are now available as
Add-ons in the Twilio marketplace. In this tutorial, learn how to harness the
power of these services by enriching SMS messages sent to a Twilio service
with IBM Watson, and route these enriched messages to a Bluemix web
application. In six easy steps, you can deploy a Bluemix application,
configure your Twilio account, and visualize the value of IBM Watson for your
text message augmentation needs!

Learn the key issues that you face with conversational design in
chatbots. Find out how to make your chatbot successful by looking at messaging platforms, types of interactions, and user inputs and responses.

One of the current trends in technology is the chatbot. This tutorial summarizes
the major messaging platforms, bot frameworks, and artificial intelligence (AI) services
you use to develop your chatbot applications.

Discover how a hypothetical bank uses IBM Watson services to enhance their loyalty program. By focusing on a few key services, they can gain insights into their customers' personalities and predict their behavior and preferences, allowing them to personalize rewards. Using Tone Analyzer in a live chat also helps customer representatives more accurately read the customers' tone and so the customer reps can adjust their own tone while typing.

This article describes the growing relevance of Machine Learning used in various kinds
of analytics along with an overview of Deep Learning. It provides an end-to-end process for
using Machine Learning and Deep Learning and the options for getting started on IBM® Power
Systems™.

Quickly build an app to get data from Twitter and then use the data for cognitive
insights by using IBM Watson services such as Tone Analyzer, Visual Recognition, and
Alchemy. In just a few minutes, you can create a running Python app on IBM Cloud that
analyzes pictures that are included in tweets.

Quickly build an app to get data from Twitter and then use the data for cognitive
insights by using IBM Watson services such as Tone Analyzer, Visual Recognition, and
Alchemy. You can drill down on the sentiments by analyzing the text right down to the
sentence level. You then share your findings through a bar graph of emotions.

This series guides you through creating an application that uses the
Business Rules service on IBM Bluemix and automates decisions based on the
results of the Personality Insights service. In Part 2, you develop an example
personality-driven job matching application with Play Framework, and you
deploy it on Bluemix with a custom Java build pack. The example application
demonstrates how you can run, integrate, and deploy the Personality Insights,
Business Rules, and ClearDB MySQL Database services on Bluemix.

This series guides you through creating an application that uses the
Business Rules service on IBM Bluemix and automates decisions based on the
results of the Personality Insights service. Part 1 describes how you can use
the Business Rules service to construct and deploy business rules on Bluemix,
based on the Personality Insights data. You learn how to define a Business
Rules data model from the Personality Insights data model. Finally, you learn
how to deploy and test the Personality Insights-driven Business Rules project
on Bluemix.

In Part 1 of this two-part series, learn how to create a powerful,
browser-based, document storage and search application that makes it faster
and easier to search for relevant content in your documents. The application
uses the Slim PHP micro-framework, together with Document Conversion and
Keyword Extraction services from IBM Watson. IBM Bluemix provides object
storage services and hosting infrastructure.